Data Engineer

• Data Hub Architecture – Design and implement efficient, scalable Data Hubs integrating multiple data sources.
• Mentoring & Strategy – Guide engineers, review code, and provide technical direction.
• Data Pipelines – Develop batch and real-time data processing workflows.
• Quality & Monitoring – Implement data validation, anomaly detection, and automated monitoring, taking action when needed.
• Automation & CI/CD – Enable seamless deployment and infrastructure automation.
• Security & Compliance – Ensure data governance, security, and regulatory compliance.
• Documentation – Maintain clear, structured technical documentation.

• Python, SQL – Strong data engineering and automation expertise.
• Databricks & Spark – Deep knowledge of Databricks (primary tool) and Apache Spark.
• Azure Data Stack – Experience with Data Factory, ADLS, Synapse, Azure SQL, Event Hubs.
• Real-Time Processing – Expertise in streaming data and real-time analytics.
• Automation & DevOps – Proficiency in CI/CD, Terraform, Kubernetes/AKS, Docker.
• Documentation & Communication – Clear, structured technical writing and knowledge sharing.
• Language Skills – English proficiency, minimum B2 level.

Technology Stack:

• Primary Tools – Databricks, Apache Spark, Delta Lake.
• Azure Services – Data Factory, ADLS, Azure SQL, Synapse, Azure DevOps, Event Hubs.
• Development & Automation – Python, SQL, GitHub, Terraform, Docker, Kubernetes/AKS.